Open Positions

Research Engineer

Location: San Francisco / Remote

Type: Full-time

Are you passionate about making AI models smaller, faster, and more efficient? We're looking for a Research Engineer to lead our edge AI optimization efforts and develop cutting-edge solutions for deploying models on resource-constrained devices.

What You'll Do:

• Design and implement advanced quantization techniques to compress neural networks while maintaining accuracy
• Develop novel training methodologies optimized for edge deployment scenarios
• Research and prototype new approaches to model compression, pruning, and knowledge distillation
• Collaborate with hardware teams to optimize models for specific chip architectures
• Publish findings and contribute to the broader research community
• Mentor junior engineers and drive technical excellence across the team

What We're Looking For:

• Quantization Expertise: Deep understanding of post-training quantization, quantization-aware training, and mixed-precision techniques
• PyTorch Mastery: Extensive experience with PyTorch for research and production, including custom operators and model optimization
• Model Training: Proven track record in training large-scale models, distributed training, and hyperparameter optimization
• Advanced degree in Computer Science, Machine Learning, or related field (or equivalent experience)
• Publications in top-tier ML conferences/journals preferred

Bonus Points: